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Toward an Open-Access of High-Frequency Lake Modelling and Statistics Data for Scientists and Practitioners

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Toward an Open-Access of High-Frequency Lake Modelling and Statistics Data for Scientists and Practitioners Toward an open-access of high-frequency lake modelling and statistics data for scientists and practitioners. The case of Swiss Lakes using Simstrat v2.1 Adrien Gaudard1†, Love Råman Vinnå1, Fabian Bärenbold1, Martin Schmid1, Damien Bouffard1 5 1Surface Waters Research and Management, Eawag, Swiss Federal Institute of Aquatic Sciences and Technology, Kastanienbaum, Switzerland † deceased, 2019 Correspondence to: Damien Bouffard ([email protected]) Abstract 10 One-dimensional hydrodynamic models are nowadays widely recognized as key tools for lake studies. They offer the possibility to analyse processes at high frequency, here referring to hourly time scale, to investigate scenarios and test hypotheses. Yet, simulation outputs are mainly used by the modellers themselves and often not easily reachable for the outside community. We have developed an open-access web-based platform for visualization and promotion of easy access to lake model output data updated in near real time (simstrat.eawag.ch). This platform was developed for 54 lakes in Switzerland with 15 potential for adaptation to other regions or at global scale using appropriate forcing input data. The benefit of this data platform is practically illustrated with two examples. First, we show that the output data allows for assessing the long term effects of past climate change on the thermal structure of a lake. The study confirms the need to not only evaluate changes in all atmospheric forcing but also changes in the watershed or through-flow heat energy and changes in light penetration to assess the lake thermal structure. Then, we show how the data platform can be used to study and compare the role of episodic strong 20 wind events for different lakes on a regional scale and especially how their thermal structure is temporarily destabilized. With this open-access data platform we demonstrate a new path forward for scientists and practitioners promoting a cross-exchange of expertise through openly sharing of in-situ and model data. 1 1 Introduction Aquatic research is particularly oriented towards providing relevant tools and expertise for practitioners. Understanding and monitoring inland waters is often based on in situ observations. Today, the physical and biogeochemical properties of many lakes are monitored using monthly to bi-monthly vertical discrete profiles. Yet, part of the dynamics is not captured at this 5 temporal scale (Kiefer et al., 2015). An emerging alternative approach consists in deploying long-term moorings with sensors and loggers at different depths of the water column. However, this approach is seldom used for country-level monitoring, although it is promoted by research initiatives such as GLEON (Hamilton et al., 2015) or NETLAKE (Jennings et al., 2017). It is common to parameterize aquatic physical processes with mechanistic models, and ultimately use them to understand aquatic systems through scenario investigation or projection of trends in for example a climate setting. In the last decades, 10 many lake models have been developed. They often successfully reproduce the thermal structure of natural lakes (Bruce et al., 2018). Today’s most widely referenced one-dimensional (1D) models include (alphabetic order) DYRESM (Antenucci and Imerito, 2000), FLake (Mironov, 2005), GLM (Hipsey et al., 2014), GOTM (Burchard et al., 1999), LAKE (Stepanenko et al., 2016), Minlake (Riley and Stefan, 1988), MyLake (Saloranta and Andersen, 2007), and Simstrat (Goudsmit et al., 2002). The results from these models are mainly used by the modellers themselves, and often not easily accessible for the outside 15 community. The performance of lake models is determined by the physical representativeness of the algorithms and by the quality of the input data. The latter include (i) lake morphology, (ii) atmospheric forcing, (iii) hydrological cycle (e.g. inflow, outflow and/or water level fluctuations), and (iv) light absorption. In situ observations, such as temperature profiles, are required for calibration of model parameters. To support this approach, it is important to promote and facilitate the sharing of existing 20 datasets of observations among scientists and practitioners. Conversely, scientists and practitioners should benefit from the model output, which is often ready-to-use, high-frequency and up-to-date. Yet, model output data should not only be seen as a tool for temporal interpolation of measurements. Models also provide data of hard to measure quantities which are helpful for specific analyses (e.g., the heat content change to assess impact of climate change, or the vertical diffusivity to estimate vertical turbulent transport). Models finally support the interpretation of biogeochemical processes which often depend on the 25 thermal stratification, mixing and temperature. In a global context of open science, collaboration between the different actors and reuse of field and model output data should be fostered. Such win-win collaboration serves the interests of lake modellers, researchers, field scientists, lake managers, lake users, and the public in general. In this work, we present a new automated web-based platform to visualize and distribute the near real time (weakly) output of the one-dimensional hydrodynamic lake model Simstrat through an user-friendly web interface. The current version includes 30 54 Swiss lakes covering a wide range of characteristics from very small volume such as Inkwilersee (9 x 10-3 km3) to very large systems such as Lake Geneva (89 km3), over an altitudinal gradient (Lago Maggiore at from 193 m. a.s.l. to Daubensee at 2207 m. a.s.l.) and over all trophic states (14 euthrophic lakes, 10 mesotrophic lakes and 21 oligotrophic lakes, Appendix 2 A). We focus here on describing the fully automated workflow, which simulates the thermal structure of the lakes and weekly updates the online platform (https://simstrat.eawag.ch) with metadata, plots and downloadable results. This state-of-the-art framework is not restricted to the currently selected lakes and can be applied to other systems or at global scale. 2 Methods 5 2.1 Model and workflow We use the 1D lake model Simstrat v2.1 to model 54 Swiss lakes or reservoirs (see Appendix A for details of modelled lakes) in an automated way. Simstrat was first introduced by Goudsmit et al. (2002) and has been successfully applied to a number of lakes (Gaudard et al., 2017; Perroud et al., 2009; Råman Vinnå et al., 2018; Schwefel et al., 2016; Thiery et al., 2014). Recently, large parts of the code were refactored using the object-oriented Fortran 2003 standard. This version of Simstrat 10 provides a clear, modular code structure. The source code of Simstrat v2.1 is available via GitHub at: https://github.com/Eawag-AppliedSystemAnalysis/Simstrat/releases/tag/v2.1. A simpler build procedure was implemented using a docker container. This portable build environment contains all necessary software dependencies for the build process of Simstrat. It can therefore be used on both Windows and Linux systems. A step-by-step guide is provided on GitHub. In addition to the improvements already described by Schmid and Köster (2016), Simstrat v2.1 includes (i) the possibility to 15 use gravity-driven inflow and a wind drag coefficient varying with wind speed – both described by Gaudard et al. (2017), and (ii) an ice and snow module. The ice and snow module employed in the model is based on the work of Leppäranta (2014, 2010) and Saloranta and Andersen (2007), and is further described in Appendix B. A Python script was developed to (i) retrieve the newest forcing data directly from data providers and integrate them into the existing datasets, (ii) process the input data and prepare the full model and calibration setups, (iii) run the calibration of the 20 model for the chosen model parameters, (iv) provide output results, and (v) update the simstrat.eawag.ch online data platform to display these results. The script is controlled by an input file written in JSON format, which specifies the lakes to be modelled together with their physical properties (depth, volume, bathymetry, etc.) and identifies the meteorological and hydrological stations to be used for model forcing. The overall workflow is illustrated in Figure 1. 2.2 Input data 25 Table 1 summarizes the type and sources of the data fed to Simstrat. For meteorological forcing, homogenized hourly air temperature, wind speed and direction, solar radiation and relative humidity from the Federal Office of Meteorology and Climatology (MeteoSwiss, CH) weather stations are used. For each lake the closest weather stations are used. Air temperature is corrected for the small altitude difference (see Appendix A) between the lake and the meteorological station, assuming an adiabatic lapse rate of -0.0065 °C m-1. This correction is a source of error in high altitude lakes like Daubensee for which 30 dedicated meteorological station would be needed. The cloud cover needed for downwelling longwave radiations are estimated 3 by comparing observed and theoretical solar radiation (Appendix C). For hydrological forcing, homogenized hourly data from the stations operated by the Federal Office for the Environment (FOEN) are used. For each lake, the data from the available stations at the inflows are aggregated to feed the model with a single inflow. The aggregated discharge is the sum of the discharge of all inflows, and the aggregated temperature is the weighted average of the inflows for which temperature is 5 measured. Inflow data are often missing for small or high altitude lakes (Appendix A). Missing inflows and more generally watershed data is a source of error in small alpine lakes, yet, such error can be compensated during the calibration process. The light absorption coefficient [m-1] is either obtained from Secchi depth [m] measurements (for Inkwilersee, Lake Biel, Lake Brienz, Lake Geneva,abs Lake Neuchatel, Lower Lake Zurich, Oeschinensee,Secchi Upper Lake Constance, and Sihlsee), or is set to a constant value based on the lake trophic status.
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